Retrieval of the top N matches with support vector machines
Jae‐Jin Kim, Bon-Woo Hwang, Seong–Whan Lee · 2002
Support vector machines (SVM) have been recently proposed for pattern recognition. Their basic property allows us to find a decision surface between two classes in terms of a hyperplane in a high dimensional space. In a multiclass recognition problem, SVM are used in the form of a combination of binary classifiers. However, SVM are unable to retrieve the top N matches, since they are designed to yield only one-the best match-in a multiclass problem. In other words, there is no proper similarity measurement for ordering all the classes in a given space using SVM. In this paper, we present an efficient method for the retrieval of the top N matches in a multiclass problem using SVM. For evaluation of the proposed method, we compared its result with that of a PCA algorithm in ranking the matches between classes.